Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease
The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) data...
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ftdoajarticles:oai:doaj.org/article:d3d6d15f59d6420b941437fdf2a9e505 2023-05-15T16:01:12+02:00 Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease Xiaowang Bi Wei Liu Huaiqin Liu Qun Shang 2021-01-01T00:00:00Z https://doi.org/10.1155/2021/8198552 https://doaj.org/article/d3d6d15f59d6420b941437fdf2a9e505 EN eng Hindawi Limited http://dx.doi.org/10.1155/2021/8198552 https://doaj.org/toc/2040-2295 https://doaj.org/toc/2040-2309 2040-2295 2040-2309 doi:10.1155/2021/8198552 https://doaj.org/article/d3d6d15f59d6420b941437fdf2a9e505 Journal of Healthcare Engineering, Vol 2021 (2021) Medicine (General) R5-920 Medical technology R855-855.5 article 2021 ftdoajarticles https://doi.org/10.1155/2021/8198552 2022-12-31T10:41:41Z The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) database (A total of 180 AD cases, 88 women, 92 men; 188 samples in healthy conditions (HC), including 90 females and 98 males. 210 samples of mild cognitive impairment (MCI), 104 females and 106 males). On the basis of deep learning, an early AD diagnosis system was constructed using CNN (Convolutional Neural Network) and DML algorithms. Then, the system was used to classify AD, HC, and MCI, and the two algorithms were compared for the accuracy and stability of in classification of MRI images. It was found that in the classification of AD and HC, the classification accuracy and sensitivity of the deep measurement learning model are both 0.83, superior to the CNN model; in terms of specificity, the classification specificity of the DML model was 0.82, slightly lower than that of the CNN model; and that in the classification of MCI and HC, the classification accuracy and sensitivity of the DML model was 0.65, superior to the CNN model; and in terms of specificity, the classification specificity of the DML model was 0.66, slightly lower than that of the CNN model. It suggested that the DML model demonstrated better classification effects on early AD patients. The loss curve analysis results showed that, for classification of AD and HC or MCI and HC, the DML algorithm can improve the convergence speed of the AD early prediction model. Therefore, the DML algorithm can significantly improve the clarity and quality of MRI images, elevate the classification accuracy and stability of early AD patients, and accelerate the convergence of the model, providing a new way for early prediction of AD. Article in Journal/Newspaper DML Directory of Open Access Journals: DOAJ Articles Journal of Healthcare Engineering 2021 1 7 |
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Directory of Open Access Journals: DOAJ Articles |
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language |
English |
topic |
Medicine (General) R5-920 Medical technology R855-855.5 |
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Medicine (General) R5-920 Medical technology R855-855.5 Xiaowang Bi Wei Liu Huaiqin Liu Qun Shang Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease |
topic_facet |
Medicine (General) R5-920 Medical technology R855-855.5 |
description |
The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) database (A total of 180 AD cases, 88 women, 92 men; 188 samples in healthy conditions (HC), including 90 females and 98 males. 210 samples of mild cognitive impairment (MCI), 104 females and 106 males). On the basis of deep learning, an early AD diagnosis system was constructed using CNN (Convolutional Neural Network) and DML algorithms. Then, the system was used to classify AD, HC, and MCI, and the two algorithms were compared for the accuracy and stability of in classification of MRI images. It was found that in the classification of AD and HC, the classification accuracy and sensitivity of the deep measurement learning model are both 0.83, superior to the CNN model; in terms of specificity, the classification specificity of the DML model was 0.82, slightly lower than that of the CNN model; and that in the classification of MCI and HC, the classification accuracy and sensitivity of the DML model was 0.65, superior to the CNN model; and in terms of specificity, the classification specificity of the DML model was 0.66, slightly lower than that of the CNN model. It suggested that the DML model demonstrated better classification effects on early AD patients. The loss curve analysis results showed that, for classification of AD and HC or MCI and HC, the DML algorithm can improve the convergence speed of the AD early prediction model. Therefore, the DML algorithm can significantly improve the clarity and quality of MRI images, elevate the classification accuracy and stability of early AD patients, and accelerate the convergence of the model, providing a new way for early prediction of AD. |
format |
Article in Journal/Newspaper |
author |
Xiaowang Bi Wei Liu Huaiqin Liu Qun Shang |
author_facet |
Xiaowang Bi Wei Liu Huaiqin Liu Qun Shang |
author_sort |
Xiaowang Bi |
title |
Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease |
title_short |
Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease |
title_full |
Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease |
title_fullStr |
Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease |
title_full_unstemmed |
Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease |
title_sort |
artificial intelligence-based mri images for brain in prediction of alzheimer's disease |
publisher |
Hindawi Limited |
publishDate |
2021 |
url |
https://doi.org/10.1155/2021/8198552 https://doaj.org/article/d3d6d15f59d6420b941437fdf2a9e505 |
genre |
DML |
genre_facet |
DML |
op_source |
Journal of Healthcare Engineering, Vol 2021 (2021) |
op_relation |
http://dx.doi.org/10.1155/2021/8198552 https://doaj.org/toc/2040-2295 https://doaj.org/toc/2040-2309 2040-2295 2040-2309 doi:10.1155/2021/8198552 https://doaj.org/article/d3d6d15f59d6420b941437fdf2a9e505 |
op_doi |
https://doi.org/10.1155/2021/8198552 |
container_title |
Journal of Healthcare Engineering |
container_volume |
2021 |
container_start_page |
1 |
op_container_end_page |
7 |
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1766397163299930112 |